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Artificial Intelligence & Architecture

From Research to Practice

Birkhauser

Artificial Intelligence’s (AI) encounter with Architecture is still in its infancy. However, current experiments and applications already are a testimony to their gradual intersection.

This book provides an introduction to the topic through the triple lens of History, Application, and Theory. A chronology of Architecture’s technological evolution first puts AI back in the context of the discipline. The author then presents a collection of AI’s applications in Architecture. The book finally gives the stage to contributors working at the forefront of this revolution. From Harvard to Foster & Partners, their perspectives provide a panorama of the discourse surrounding AI’s presence in the field.

Halfway between research and practice, this book offers to unveil the promise and challenges AI holds for Architecture.

With the contribution of Foster + Partners' ARD Group, the City Intelligence Lab, Kyle Steinfeld, Andrew Witt, Alexandra Carlson & Matias del Campo, Caitlin Mueller & Renaud Danhaive, Immanuel Koh, and Carl Christensen.

 

Copyright year: 2022

Pages: 208

Language: English

Published: 07 Mar 2022

on Birkhauser's Website

on Amazon

AI & Architecture

Towards a New Approach

Thesis at Harvard University. Advisor: Prof. Andrew Witt.

The advent of Artificial Intelligence as a discipline has been permeating countless fields, bringing means and methods to previously unresolved challenges, across industries. The fusion of this new techno-science with Architecture is still in its early days but offers promising results. Our thesis proposes to evidence its potential as it is applied to Architecture. More than a mere opportunity, it is to us, shaping a promising discipline per se. Specifically, we offer to apply AI to floorplans analysis, and generation. Our ultimate goal is two-fold: offer a proper classification methodology of floorplans, able to tackle diversity and quantity, while creating a framework for machine learning-based floorplan generation.

Floorplans are indeed a high-dimensional problem, at the crossroad of quantifiable technics, and more qualitative properties. The study of architectural precedent remains too often a hazardous process, that negates the richness of the number of existing resources while lacking in analytical rigor. We offer here a methodology, inspired by current Data Science methodologies, to qualify plans, both through their style and their organization.

At the heart of this project, lies the necessity of inventing meaningful metrics to qualify and classify floorplans. Through the creation of 6 metrics, we propose a framework that captures architecturally relevant parameters of floorplans.

On one hand, Footprint Shape, Orientation, Thickness & Texture are 3 metrics capturing the essence of a given floorplan’s style. On the other hand, Program, Connectivity and Circulation will capture the essence of a given floorplan organization.

The advent of automated classification is in fact the bedrock of the machine learning practices. Our thesis offers to leverage our database of classified plans to evidence the possibility of such tools applied to floorplan generation. Our methodology follows two main intuitions (1) the creation of floorplans is a non-trivial technical challenge, that encompasses standard optimization technics. (2) The design of space is a sequential process, requiring successive design steps across different scales (urban scale, building scale, unit scale). We attempt to capture these two realities by using nested Generative Adversarial Neural Networks. The use of such models will enable us to capture more complexity across encountered floorplans, while breaking down the complexity of the tackled problems into successive steps. To each step will correspond a given model, trained for this particular task.

Overall, our thesis will evidence the possible back and forth between human and machines, that permeates the architectural discipline today. The machine that was once the extension of our pencil, can today be leveraged to map architectural knowledge, and be trained to assist us creating viable design options.

on Towards Data Science

on Archinect

on ArchDaily

Architecture as a Graph

A Computational Approach

with Jeffrey Landes, Hakon Fure & Hakon Dissen

The design of floorplans can leverage machine intuition to generate and qualify potential design options. In this article, we address a specific abstraction of space: adjacency. Any floorplan carries its own embedded logic; in clear, the relative placement of rooms and their connections is driven by a certain logic of interdependence, and yields varying qualities across space. For instance, the presence of a room will condition the existence of other rooms, as well as the position of openings between them. First, we attempt here to qualify adjacencies of existing floorplans, to assess the relevance of adjacencies among rooms. We later turn to Bayesian modeling to generate adjacency graphs, either freely or under set constraints. By qualifying and generating, our hope is to investigate both sides of the same problem: the understanding of relationships among neighboring spaces.

on Towards Data Science

Space Layouts & GANs

GAN-enabled Floor Plan Generation

Apartment layout is a challenging yet fundamental task for any architect. Knowing how to place rooms, decide their size, find the relevant adjacencies among them, while defining relevant typologies are key concerns that any drafter takes into account while designing floor plans. In this article, we propose showcasing possibilities offered by Generative Adversarial Neural Networks models (GANs), and their ability to generate relevant floor plan designs. In short, we turn to GAN models, and more specifically Pix2Pix, to help us design housing floor plans, given a set of initial conditions & constraints.

on Towards Data Science

Architecture & Style

A New Frontier for AI in Architecture

We build here upon a previous piece, where our emphasis revolved around the strict organization of floor plans and their generation, using Artificial intelligence, and more specifically Generative Adversarial Neural Networks (GANs). As we refine our ability to generate floor plans, we raise the question of the bias intrinsic to our models and offer here to extend our study beyond the simple imperative of organization. We investigate architectural style learning, by training and tuning an array of models on specific styles: Baroque, Row House, Victorian Suburban House, & Manhattan Unit. Beyond the simple gimmick of each style, our study reveals the deeper meaning of stylistic: more than its mere cultural significance, style carries a fundamental set of functional rules that defines a clear mechanic of space and controls the internal organization of the plan. In this new article,we will try to evidence the profound impact of architectural style on the composition of floor plans.

on Towards Data Science

Suggestive Computer-Aided Design

Assisting Design Through Machine Learning

In collaboration with Thomas Trinelle

The utilization of machine-based recommendation has been leveraged in countless industries, from suggestive search on the web, to photo stock image recommendation. At its core, a recommendation engine can query relevant information -text, images, etc- among vast databases and surface it to the user, as he/she interacts with a given interface. As large 3D data warehouses are being aggregated today, Architecture & Design could benefit from similar practices.

In fact, the design process in our discipline happens mostly through the medium of 3D software (Rhinoceros 3D, Maya, 3DSmax, AutoCAD). Might it be through CAD software(Computer-Aided Design), or today BIM engines (Building Information Modeling), Architects constantly translate their intention into lines and surfaces in 3D space. Suggesting relevant 3D objects, taken from exterior data sources, could be a way to enhance their design process.

This is the goal of this article: study and propose a way to assist designers, through “suggestive modeling”. As architects draw in 3D space, an array of machine-learning-based classifiers would be able to search for relevant suggestions and propose alternative, similar or complementary design options.

To that end, taking inspiration from precedents in the field of 3D shape recognition & classification, we come up with a methodology and a toolset able to suggest models to designers as they draw. Our goal is, in fact, twofold: (1) to speed up 3D-modeling process with pre-modeled suggestions, while (2) inspiring designers through alternative or complementary design options.

on Towards Data Science

The Advent of Architectural AI

A Historical Perspective

The practice of Architecture, its methods, traditions and know-how are today at the center of passionate debates. Challenged by outsiders, arriving with new practices, and questioned from within, as practitioners doubt of its current state, Architecture is undergoing a truly profound (r)evolution.

Among the factors that will leave a lasting impact on our discipline, technology certainly is one of the main vectors at play. The inception of technological solutions at every step of the value chain has already significantly transformed Architecture. The conception of buildings has in fact already started a slow transformation: first by leveraging new construction technics, then by developing adequate softwares, and eventually today by introducing statistical computing capabilities (including Data Science & AI). Rather than a disruption, we want to see here a continuity that led Architecture through successive evolutions until today. Modularity, Computational Design, Parametricism and finally Artificial Intelligence are to us the four intricated steps of a slow-paced transition. Beyond the historical background, we posit that this evolution is the wireframe of a radical improvement in architectural conception.

on Towards Data Science

KI und Architektur

Der entwerfende Computer

Wie lassen sich Computer, die früher lediglich eine Art erweiterter Zeichenstift waren, heute dazu nutzen, architektonisches Wissen abzubilden? Wie lassen sie sich mit Hilfe maschinellen Lernens trainieren, Architekt*innen bei der Entwurfsarbeit zu unterstützen?

Die hier vorgeschlagene Methode basiert auf einem dreistufigen Prozess: (I) Generierung von Grundrissen, das heißt Optimierung der Erzeugung sehr unterschiedlicher Grundrissentwürfe in großer Zahl, (II) Qualifizierung von Grundrissen, das heißt Entwicklung einer geeigneten Klassifizierungsmethode, und (III) Bereitstellung der Möglichkeit für die Nutzer*innen, die erzeugten Entwürfe zu durchsuchen.

Der Prozess zielt auf eine bessere Differenzier-, Qualifizier- und Modulierbarkeit von Ergebnissen im Vergleich zu bisherigen Programm­anwendungen aus dem Feld der KI-gestützten Raumplanung.

on ARCH +

Urban Tech On The Rise

When Machine Learning Disrupts the Real Estate Industry

with Daniel Fink & Pamella Gonçalves

The practice of urban analytic is taking off in the real estate profession. Data science and algorithmic logic are close to the forefront of new urban development practices. “How close?” is the question, but experts consider that digitization will go far beyond intelligent building management systems. New analytical tools with predictive capabilities will dramatically affect the future of urban development, reshaping the real estate industry in the process.

on the Harvard Real Estate Review

on the Veolia Institute FACTS Review

Metabolism(S)

Space Flexibility in The 21st Century

“Flexibility” in architecture refers to the ability of a building to continuously adapt its space layout and even its structure to evolving needs. Stemming from the Modernist movement’s dream which emerged in Japan in the 60’s, the ideal of buildings as constantly evolving entities blends together three main aspirations: the need for a more efficient built environment, an answer to urban centers densification and the humanist promise of a city that would adapt to its citizens.

Buildings are no exception to the rule of supply and demand. They quite naturally go through uneven utilization cycles with ups & downs that can be explained by a broad set of reasons: tenants’ volatility, daily traffic, business seasonality or simply competitive rivalry. But, in contrast to this cyclical nature of use, the real estate industry is stuck with rigidity: the physical rigidity of space and the contractual rigidity of leases. As a result, space is far from being used efficiently. And underperforming spaces are depleting economic value. Given this spatial inefficiency, tuning spaces on a monthly, daily – or even an hourly – basis to achieve greater efficiency seems quite legitimate. At the same time, as city centers increasingly densify, finding available space is a growing concern. Identifying under-utilized spaces and revamping them to match demand is becoming a game-changing paradigm. Finally, as the digital revolution carries the promise of a more personalized user experience, buildings might just be on the verge of following a similar trend. Through constant remodeling and reprogramming, buildings could be tailored to their users’ needs, comfort, and expectations. All in all, building flexible spaces aims to create a more relevant built environment.

However, achieving ‘’architectural flexibility’’ is a challenge that a number of investigations have tried to take up over the past century. From the early stage of the Japanese Metabolist movement to the formal flexibility of contemporary architecture, architects have progressively enshrined the principle of space plasticity. But, by and large, they have rather turned it into a style away from its initial ambition to make functional flexibility an actual operating principle. Today, however, independently of any disciplinarian consideration, the emergence of Big Data powered by algorithmic and semi-automation, may put the promise of architectural flexibility within our reach. There are three good reasons to believe it is no longer just a utopic ideal. First, data and analytics could enable the built environment to better understand and forecast space utilization. Second, semi-automation could help adapt space layouts in near-to-real time, while optimizing users’ comfort and space efficiency. And three, society's fast-moving evolution, including consumers' behavior mutation, could timely leverage the disruptive potential of technologies. On this ground, we propose a revived architectural answer: The Synaptic Building. The Synaptic Building and our manifesto, rooted in the Metabolist tradition, positioning space flexibility as the corner-stone of the 21st-century architectural practice.

on ArchDaily

on KoozArch

on the Harvard Real Estate Review